Exposure to environmental pollutants selects for xenobiotic-degrading functions in the human gut microbiome

Environmental pollutants from different chemical families may reach the gut microbiome, where they can be metabolized and transformed. However, how our gut symbionts respond to the exposure to environmental pollution is still underexplored. In this observational, cohort study, we aim to investigate the influence of environmental pollution on the gut microbiome composition and potential activity by shotgun metagenomics. We select as a case study a population living in a highly polluted area in Campania region (Southern Italy), proposed as an ideal field for exposomic studies and we compare the fecal microbiome of 359 subjects living in areas with high, medium and low environmental pollution. We highlight changes in gut microbiome composition and functionality that were driven by pollution exposure. Subjects from highly polluted areas show higher blood concentrations of dioxin and heavy metals, as well as an increase in microbial genes related to degradation and/or resistance to these molecules. Here we demonstrate the dramatic effect that environmental xenobiotics have on gut microbial communities, shaping their composition and boosting the selection of strains with degrading capacity. The gut microbiome can be considered as a pivotal player in the environment-health interaction that may contribute to detoxifying toxic compounds and should be taken into account when developing risk assessment models. The study was registered at ClinicalTrials.gov with the identifier NCT05976126.

RF classification across the three tested scenarios.We report the top-20 features (taxa) associated with a higher (in red) or lower (in green) impact for each couple of impact area.

Analysis of dioxins (PCDD/Fs) and polychlorinated biphenyls (PCBs)
The determination of PCDD/Fs and PCBs in human blood serum was carried out using a modified analytical method described by Brasseur et al. [1] The method allowed the determination of 17 PCDD/F and 12 DL-PCB congeners showing the highest toxicity for humans [2] and of 6 NDL-PCBs (PCBs 28, 52, 101, 138, 153 and 180).A volume of about 10-20 mL of serum was weighed, freezedried and extracted by an Accelerated Solvent Extraction (Dionex ASE 350, Thermo Fisher Scientific) system using a mixture of n-hexane/acetone as the solvent.The extract was cleaned up using an Extrelut NT3 column acidified with sulphuric acid 96% and eluted with a n-hexane/toluene mixture and subsequently with a florisil solid phase extraction (SPE) cartridge (Waters, Milford, MA, USA) eluted with dichloromethane.
For the HRGC-HRMS analysis of PCDD/Fs and PCBs, a DFS Magnetic Sector HRGC-HRMS system (Thermo Fisher Scientific, Waltham, MA USA) was used.A volume of 1 µL for both PCDD/Fs and PCBs was injected into the GCs in splitless mode, the temperature of the inlets was set at 280 °C.
The column used in the analysis of PCDD/Fs was a fused silica capillary column TR-1 (60 m × 0.25 mm i.d.× 0.1 μm, Thermo Fisher Scientific, Waltham, MA USA).The oven temperature was initially set at 100°C, which was maintained for 2 min; it was then increased to 220°C at a rate of 10°C min - 1 , maintained for 10 min; and then increased at a rate of 5°C min -1 to 235°C and finally, after 7 min increased to 315°C at a rate of 18°C min -1 .
The column used in the analysis of PCBs was a fused silica capillary column HT8 (60 m × 0.25 mm i.d.× 0.25 μm, SGE Analytical Science, Victoria, Australia).The oven temperature program was 90°C for 1 min, rate 4 °C min -1 until 180°C, then rate 37.5°C min -1 until 285°C and finally increased to 320°C at 3°C min -1 .
The PCDD/F and PCB congeners were quantified using the isotope dilution method adding for each congener the corresponding 13 C12-isotope compound.For this purpose, before the extraction process, each serum sample was spiked with a standard solution containing the 13 C12-labeled congeners.The data acquisition of the HRGC-HRMS analysis was performed using the multiple ion detection (MID) mode monitoring two isotopic masses for each PCDD/F and PCB congener to be quantified.
The concentrations of individual PCDD/F and DL-PCB congeners were expressed in pg g -1 on a lipid basis.The sums of 17 PCDD/F and 12 DL-PCB congeners were expressed in pg WHO-TEQ g -1 on lipid basis and were obtained from the toxic equivalent (TEQ) concentrations calculated using the toxic equivalent factors (TEFs) established for human risk assessment by the World Health Organization (WHO) [3].
The NDL-PCB congener concentrations and the sum of six NDL-PCBs were expressed in ng g -1 on lipid basis.
For each analyte, accuracy and precision were assessed by internal quality control and by using certified reference materials.
Superpure grade nitric acid 69% (v/v) was purchased from VWR International (Belgium).High purity deionised water (resistivity 18.2 MΩ cm) was produced in-house using a purification system Arium® pro (Sartorius, Germany).All glassware were soaked in a solution of nitric acid (10% w/v) then rinsed with high-purity water and dried prior to use.
For the analysis, 500 μL of serum was diluted 1:10 (v/v) with nitric acid 69 %.Calibration standard solutions and internal standards were prepared by successive dilution of a high purity ICP multielement calibration standard solution of all 19 trace elements at 1000 mg L -1 obtained from Perkin Elmer (Norwalk, CT).A five-point calibration curve at suitable ranges (0.1-100 µg L -1 ) was prepared daily in nitric acid 0.5% v/v for each element of interest and the internal standard was added on-line.
The correlation coefficient (R 2 ) of calibration curves for all the trace elements was always greater than 0.99 showing a good linear relationship throughout the selected ranges of concentration.Each sample was analysed in duplicate, and the mean concentration was used in all statistical analyses.
The analytical method was validated by an in-house quality control procedure and appropriate quality assurance procedures and precautions were implemented in order to ensure the reliability of the results.Chemical blank determinations were analyzed for each very sample, to check for possible contamination.The limit of quantification (LOQ) for all the elements was calculated on the basis of the standard deviation of the intensity of twenty reagent blanks.

Metagenomics data analysis
Human reads were removed using the Human Sequence Removal pipeline developed within the Human Microbiome Project by using the Best Match Tagger (BMtagger; https:// hmpdacc.org/hmp/doc/HumanSequenceRemoval_SOP.pdf).Then, non-human reads were qualityfiltered using PRINSEQ 0.20.4,trimming reads at the first occurrence of a base with a Phred score < 15.Reads shorter than 75 bp were discarded.Number of reads for each sample is reported in Supplementary Data 1.High-quality reads were imported in mOTUs2 [4] to obtain species-level, quantitative taxonomic profiles.The standard mOTUs database was used for taxonomic assignment.
High-quality reads were assembled using MEGAHIT v. 1.2.2 [5] and contigs <1000 bp were discarded.Genes were predicted from contigs by using MetaGeneMark v. 3.26 [6].Assembly results are reported in Supplementary Data 5.We specifically focused on genes involved in the degradation pathways or in the resistance to dioxins and other environmental pollutants.Predicted genes were aligned (using DIAMOND v. 2.0.4 [7]) against genes coding for enzymes involved in persistent organic pollutants (POPs) biodegradation (as reported in the mibPOPdb [8], release 30.11.2021) and resistance to heavy metals (BacMet database v. 2.0 [9]).An e-value cutoff of 1e -5 was applied, and a hit was required to display >95% of identity over at least 50% of the query length.To obtain the gene abundance, short reads were mapped to the genes using Bowtie2 and the number of mapped reads was normalized using the RPKM method (reads per kilo-base per million mapped reads [10]).
Microbial gene richness was estimated as described previously [11].
Contigs (>1000 bp) were also binned using MetaBAT2 [12] v. 2.12.1, and Metagenome Assembled Genomes (MAG) quality was estimated with CheckM [13] v. 1.1.3.Only MAGs with >50% completeness and <5% contamination were retained for further analyses.MAGs binned in this study were clustered to a genomic database including high-quality MAGs previously reconstructed from human metagenomes and NCBI RefSeq genomes using PhyloPhlAn3.0[14].Pairwise genetic distances between genomes were calculated using Mash (version 2.0; option "-s 10000" for sketching;).A Mash distance <5% from any of the database genomes was considered to place the MAG within the relative Species-level Genome Bin (SGB).When a MAG showed > 5% distance from any reference genomes, it was considered a novel species (unknown SGB, uSGB), and the taxonomic assignment was made at genus (> 5% and < 15% distance), family (> 15% and < 25% distance) or phylum (> 25% distance), using thresholds previously reported [15].RAxML 8.0 was used to generate a phylogenetic tree, including one MAG for each SGB, which was visualized in iTOL [16] v. 5.5.1.A list of the MAGs reconstructed is reported in Supplementary Data 3.
The identification of BacMet and mibPOP in publicly available metagenomes was performed by considering 3,769 subjects from 24 datasets, collected in a previously published repository [17].The datasets included case-control studies composed by healthy adult controls and subjects with different

Figure S1 .
Figure S1.Box plots showing the abundance in the habitual diet of the main macronutrients, in

Figure S2 .
Figure S2.Violin plots showing Jaccard's distance based on gut microbiome composition between

Figure S4 .
Figure S4.Biplots of interactions between microbes and dioxins (A) or metals (B), as estimated by

Figure S8 .
Figure S8.Box plots showing the abundance of genes related to benzoate degradation (log Reads Per

Figure S9 .
Figure S9.Box plots showing the plasmatic concentration of different heavy metals in subjects from

Figure S10 .Figure S11 .
Figure S10.Box plots showing the number of genes from mibPOP and BacMet databases found in